Communication Graph Role Inference for Cloud Micro-Segmentation
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Solution Overview
Problem
Existing systems lack a comprehensive view of network communications within cloud computing subscriptions, which is crucial for improving security and optimizing network performance, but current methods to achieve this visibility often impact resource cost and performance negatively.
Innovation Solution
A role inference pipeline that analyzes communication graphs using adjacency and node features, combined with domain knowledge and partial labeling, to infer roles of nodes, reducing dimensionality and generating embeddings for improved visualization and micro-segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive network communication monitoring is implemented to improve security and performance visibility, then network security and performance optimization are improved, but resource cost and performance are adversely impacted
Solution Approach 1:
The patent introduces a role inference pipeline as an intermediary system that analyzes communication graphs to infer functional roles of cloud resources. This intermediary layer provides comprehensive network visibility and security monitoring without requiring direct interference with the actual network traffic or computing resources, thereby avoiding performance degradation while maintaining security monitoring capabilities
Solution Approach 2:
The patent replaces traditional mechanical network monitoring methods that directly intercept and analyze network packets with a computational approach using graph neural networks and role inference algorithms. This substitution allows for indirect observation of network communications through communication graphs, reducing the overhead and performance impact associated with traditional packet capture and analysis methods
2Reliability
If comprehensive network communication monitoring is implemented to improve security and performance visibility, then network security and performance optimization are improved, but resource cost is adversely impacted
Solution Approach 1:
The role inference pipeline serves as a cost-efficient intermediary that derives security insights from communication patterns rather than requiring expensive dedicated monitoring hardware or extensive packet capture infrastructure. By inferring resource roles from communication graphs, the system achieves comprehensive security visibility at lower computational and financial cost
Solution Approach 2:
The patent creates a virtual copy of the network communication structure in the form of communication graphs, which are then analyzed to infer resource roles. This copying approach allows security analysis to be performed on the graph representation rather than requiring direct manipulation of actual network traffic, reducing the computational resources and costs associated with real-time monitoring
Data Source
AI summary
A data processing system implements receiving telemetry data from a plurality of nodes of a cloud-based computing environment; analyzing the telemetry data using a communication graph pipeline to generate a communication graph representing communication among the plurality of nodes of the cloud-based computing environment; analyzing the communication graph using a role inference pipeline to infer roles of the plurality of nodes of the cloud-based computing environment included in the communication graph and output inferred roles for the plurality of nodes; and performing one or more actions on the communication graph based on the inferred roles for the plurality of nodes.